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Electronic copy available at: http://ssrn.com/abstract=1977116Electronic copy available at: http://ssrn.com/abstract=1977116
Social Constraints, Agency, Inter-organizational Tie Formation and Knowledge Diffusion
Jason Greenberg, PhD
Leonard N. Stern School of Business, NYU
40 West 4th Street, Suite 706
New York, New York 10012
Phone: (212) 998-0229
David M.J. Lazer, PhD
Northeastern University
Departments of Computer Science and Political Science
and
Program on Networked Governance
John F. Kennedy School of Government
Harvard University
Cambridge, MA 02138
Maria C. Binz-Scharf, PhD
The City College of New York
Department of Economics and Business
160 Convent Avenue
New York, NY 10031
Ines Mergel, PhD
Department of Public Administration and International Affairs
Maxwell School of Citizenship and Public Affairs
Syracuse University
436 Crouse-Hinds Hall
Syracuse, NY 13244
Electronic copy available at: http://ssrn.com/abstract=1977116Electronic copy available at: http://ssrn.com/abstract=1977116
Social Constraints, Agency, Inter-organizational Tie Formation and Knowledge Diffusion
ABSTRACT
Social capital is currently one of social structure‘s most prominent and debated manifestations.
However, we have a limited understanding of how social ties as the basis of social capital form
in the first place. From one perspective social capital is viewed as: ―investment in social relations
with expected returns in the marketplace‖ (Lin 2001, p. 19). A second perspective on social
capital formation stresses contextual and environmental features beyond the control of
individuals that may yield benefits Both perspectives are based on premises implicating various
motives and structural constraints pertaining to relationship formation including: exchange,
power, and dependency; legitimacy seeking or preferential attachment based on status or
prestige; homogeneity or homophily and related selection processes; propinquity; or cultural or
institutional forces. These categories of mechanisms do not, however, specify a model of how
social relationships as social capital are formed in the first place.
If social capital results from ―investment strategies,‖ it is important to determine what these
strategies are. If social capital originates from structural factors beyond individual control it is
important to clarify what mechanisms lead to tie formation within social structures.
The objective of this research is to specify mechanisms of social tie formation and reinforcement
by peering inside the black-box of foci (Feld 1981) in which social ties are formed. We do so by
focusing on the structural contexts within which individual (micro-level) ―corporate actors‖ form
social relationships for knowledge acquisition that results in macro-level knowledge sharing. A
mixed-method analytical approach is employed to this end. Findings illustrate how the subtleties
of social structure define the parameters within which social relationships are (strategically)
formed.
1
Social Constraints, Agency, Inter-organizational Tie Formation and Knowledge Diffusion
INTRODUCTION
Social capital is currently one of social structure‘s most prominent and debated
manifestations (Bourdieu 1985; Portes and Sensenbrenner 1993; Portes 1998, 2000; Lin 2001),
and its purview has been extended to various levels of social life from individuals to
organizations to states and countries (Gulati 1999; Putnum 2000; Ingram, Robinson, and Busch
2005; Powell, White, Koput, and Smith 2005; Paxton 2007). Defining and describing what social
capital entails and how it relates to (un)desirable outcomes has thus motivated substantial
research (see Portes 1998; Mouw 2003, 2006 for reviews). However, we have a limited
understanding of how social ties as the basis of social capital form in the first place (Sorenson
and Stuart 2008). The objective of this research is to address this gap in the literature.
There are two primary views on the basis of social capital. From one perspective social
capital is viewed as: ―investment in social relations with expected returns in the marketplace‖
(Lin 2001, p. 19). Consistent with this view, Bourdieu (1985) argued that social capital arises
from individual investment strategies of human and cultural capital in sociability. A second
perspective on the origins of social capital stresses contextual and environmental features largely
beyond the control of individuals. How social environments shape residents‘ life chances is an
example (e.g., Wilson 1987; Mayer and Jencks 1989; Jencks and Mayer 1990; Sampson 2001;
Sampson, Morenoff, and Gannon-Rowley 2002). The mechanisms linking macro social forces to
individual outcomes can be blunt and blatant or sharp and subtle (e.g., Small 2009). However, a
cornerstone of structuralist approaches is the idea that social context and the capital it affords are
enabling or constraining above and beyond individual action (sees generally Sampson 2008).
Individuals begin life with varied levels of access to human capital, social networks, and a host
of other productive or destructive conditions simply by virtue of whom their parents are and
2
where they live (Loury 1989, p. 272). These initial conditions, in turn, have a bearing on
subsequent networking opportunities and outcomes, as well as human capital development
(Coleman 1988).
Both of these overarching categories of social capital formation presuppose various
motives for relationship formation and activation including: exchange and dependency (Homans
1958, 1961; Blau 1964; Yuchtman and Seashore 1967; Benson 1975; Cook 1975; Pfeffer and
Salancik 1978; for a review see Cook and Whitmeyer 1992); legitimacy seeking or preferential
attachment (e.g., Young and Larson 1965; Stuart 1998); or homogeneity or homophily and
related social attraction and selection processes (McPherson, Smith-Lovin, and Cook 2001; see
also Rauch 1997). Structural constraints and facilitators include propinquity (e.g., Powell, White,
Koput, and Smith 2005; Sorenson and Stuart 2008) and cultural or institutional forces (Saxenian
1994; e.g., Almeida and Kogut 1999; Small 2009). Motives are, however, almost never
observed; rather, they are extrapolated based on context. The motive compelling organizations to
form relationships may presumably be resource dependencies (e.g., Pfeffer and Salancik 1978)
and thus underlying economic logics of utility maximization generally defined. Similarly,
structural features such as propinquity may lead to tie formation due to search cost minimization,
homophily, or a higher likelihood of chance encounters. These presumed motives do not,
however, reveal how social relationships as social capital are formed in the first place.
If social capital results from ―investment strategies,‖ it is important to determine what
these strategies are. This is not an easy task because the formation of a tie today is often the
result of preexisting ties (see, e.g., Gulati and Gargiulo 1999). This endogeneity, as well as the
opacity of network content (Burt 2008, p.953) and actors‘ motivations result in considerable
ambiguity about the origin of social ties as social capital. On the other hand, if social capital
3
originates from structural factors beyond individual control it is important to clarify what
mechanisms lead to tie formation within social structures.
The objective of this research is to identify mechanisms of social tie formation and
reinforcement by peering inside the black-box of foci (Feld 1981) in which knowledge sharing
ties are formed (see Figure 1 below). We do so by focusing on the structural contexts within
which individual (micro-level) ―corporate actors‖ 1
form social relationships (link 3 in the figure)
for knowledge acquisition that yield macro-level knowledge transmission (link 4 in the figure)
(Alexander 1987; Blau 1987; Coleman 1987, 1994, 1998).
This focus on the action of corporate actors is important. As noted above, significant
attention has been devoted to the patterns and consequences of the boundary spanning activities
of macro social entities such as organizations. This research treats organizations as social actors
in and of themselves that engage in this boundary spanning activity. This focus is sensible when
describing the patterns of inter-organizational ties. However, it is not well-suited to reveal how
organizations develop social capital and span boundaries in the first place. Observing that
organizations in close proximity are more likely to have a joint venture agreement, for example,
does not reveal how the agreement forms. Moreover, using individual-level mechanisms as
explanations for patterns among macro-level social entities necessitates some consideration. This
follows because organizations qua organizations do not run into each other at foci and decide to
form joint ventures. Corporate actors make these links in concrete ways by and for themselves
and on behalf of their organizations. And not all equally situated and endowed corporate actors
1 We thus assume that organizations have institutionally defined and delegated rights, responsibilities, and
incentives that enable their (micro level) agents to interact, engage, and establish formal and informal ties with other
macro social entities (organizations) by interacting with other micro level corporate actors (Blau 1964; Coleman
1990, pp. 12-13; Ch. 16).
4
make these links in similar foci. Discerning differences and similarities across similarly situated
corporate actors is thus important.
-----------------------------------
Insert Figure 1. about here
-----------------------------------
We employ a mixed-methods approach to do so. In the next section we describe the
strategic research site—professional forensic DNA scientists employed in state crime
laboratories. Survey data are then used to paint a picture of the social structure of knowledge
sharing relationships and constraints among these labs across states. We employ these
quantitative methods for descriptive rather than predictive purposes, so formal hypotheses are
not proposed. Focus is then devoted to the qualitative evidence, which is particularly well suited
to inform how knowledge sharing relationships form within foci, which is the novel theoretical
contribution offered here. We conclude with a summary and discussion of theoretical
implications and potential extensions.
STRATEGIC RESEARCH SITE: FORENSIC DNA ANALYSIS LABORATORIES
The objective of this research is to identify and describe the mechanisms leading to social
tie formation between corporate agents that lead to knowledge diffusion between macro-social
entities. We thus require a strategic site in which tasks are inherently complex and require more
than codified instructions—that is, a context in which knowledge is embedded in both individual
actors and the specific social systems in which they work (on organizational knowledge see, e.g.,
Cook and Brown 1999; Tsoukas and Vladimiou 2002). We also require a setting in which
knowledge is constantly changing, which implies that actors must constantly update their
knowledge.
5
We identified an institutional field that evidenced these characteristics: government-run
laboratories that analyze forensic DNA. The work of forensic scientists is knowledge-intensive,
specialized, and highly complex, and it is subject to constantly changing technology (Self-
identifying citation omitted).
The minimum educational requirement for forensic scientists2 is a Bachelor of Science
degree, and most scientists hold degrees in chemistry or biology. Many DNA analysts also have a
Master of Science degree (often required for supervisory positions) in chemistry, biology, or
forensic science. Supervisors often have doctorate degrees. Given the educational requirements
and the subsequent task-specific human capital required, this field evidences many of the
characteristics of science and academia as individuals must constantly update their knowledge
and skills, and this learning process often occurs through networks and institutional mechanisms
such as professional conferences.
There are DNA laboratories at the federal, state, and local levels. These labs are generally
non-hierarchically arranged. State labs generally do not have authority over local labs, nor do
federal over state labs. However, there are elements of hierarchy within the system with respect
to: (1) the infrastructure (the FBI promulgates rules regarding access to the national database); (2)
resources (e.g., lab funding); and (3) federal regulation.
Neither the size of a single lab nor the number of labs in a given state necessarily reflects
the size (or population) of the host state. Rather, it is a result of historical development due to:
state and local jurisdictions that attribute more or less importance to DNA analysis; institutional
2 The field of forensic science is defined by the American Association of Forensic Scientists as ―the application of
the natural sciences to matters of the law.‖
6
entrepreneurship (a successful lab is more likely to expand); and geographical necessity (states
with greater land area may see the need for geographically dispersed labs).
Although procedural rules and regulations vary between labs and across jurisdictions, the
core work of forensic scientists in government crime labs is essentially the same. It includes:
determining the usefulness of a DNA sample provided by crime scene investigators; the
preparation of a sample for analysis; the interpretation of DNA mixtures (e.g. when the DNA of
two or more individuals is present in a sample); creating DNA profiles; and uploading the DNA
profiles of convicted offenders into a database. The vast majority of procedures involved in these
tasks are codified in painstaking detail in the labs‘ manuals, which are constantly updated to
reflect the rapid change of technological advancement and resulting new procedures.
Furthermore, the FBI issues regulations that govern access to the Combined DNA Index System
(CODIS). CODIS is software that links and operates local, state, and national databases of DNA
profiles from convicted offenders, crime scene evidence from unsolved crimes, and missing
persons. CODIS allows for the electronic comparison of these profiles. The objective of the
database is to link known individuals to crimes, as well as crimes to each other. The state
component of the database is known as the State DNA Index System (SDIS), where a subset of
these profiles is also uploaded into the National DNA Index System (NDIS). Lab procedures are
regularly audited under the auspices of the FBI to ensure compliance with standards set by the
National Institute for Standards and Technology (NIST). Compared with private sector fields,
this setting is thus ne in which there are considerable pressures towards sharing (DiMaggio and
Powell 1983). Inter-lab competitive forces for relationships should thus be subdued.
Despite this high level of codification, a considerable amount of ambiguity and discretion
remains in the work of DNA forensic scientists. For example, the processing of DNA mixtures
7
requires interpretation. There are a number of statistical that can be used in similar cases, and
different labs prescribe the use of different statistics (Butler 2005). Another example is the
adoption of newly available technologies. In addition to decisions concerning what technology to
use, there are different private equipment vendors to choose from with varying levels of quality
and support. The choice of which vendors to use is thus consequential as it can significantly
influence both the efficiency and efficacy of a lab. Finally, there is a wide range of discretionary
management issues ranging from human resource issues to decisions concerning the design of
new laboratory space.
In summary, DNA laboratories in the United States offer a good site within which to
study knowledge sharing across salient macro-structural boundaries: First, there is a rather
limited and clearly definable population of laboratories (about 180), with at least one laboratory
in every state; second, each laboratory, and individuals within laboratories, have significant
discretion concerning boundary spanning and sharing activities; and, third, each lab‘s ability to
fulfill its responsibilities is contingent on constantly acquiring and internalizing new knowledge.
QUALITATIVE AND QUANTITATIVE DATA COLLECTION AND ANALYSIS
Qualitative data collection. DNA labs exhibit variation in terms of size, location,
structure, and expertise. To ensure that we developed a comprehensive understanding of the field
of enquiry we sampled firms across various strata. Respondents for this study were selected
through purposeful sampling (Yin 1994) according to the professional roles held by members of
the community in a single case study design with multiple sites. This method of sampling allows
for comparability between the respondents while allowing for variation across labs types to
achieve representativeness.
Participant recruitment began with state CODIS administrators. We assumed that these
individuals had a greater need to connect to their peers in other labs because there is only one
8
CODIS administrator per lab, and CODIS is the mechanism used for inter-state sample
comparisons. After a first round of telephone interviews, two of the authors attended several
CODIS conferences and interviewed state administrators in-person. Attending these conferences
allowed the authors to gain insights into the interaction patterns of CODIS administrators. Early
participants in the study led us sequentially to additional important respondents within the
community (Miles and Huberman 1994). We stopped recruiting additional respondents when we
started getting very similar responses and therefore had reached saturation in our sample. Our
final pool of respondents consisted of 33 individuals, from 30 labs and 26 states.
We conducted semi-structured, open-ended interviews with these informants that lasted
between thirty minutes and two hours. The interviews covered the following topics: Description
of work function and work environment; knowledge required for the job; sources of knowledge;
and nature, extent, and methods of community engagement. As our data collection proceeded,
we also included specific questions about the nature of ties to individuals in other labs. Table 1
below provides a description of the qualitative interviewees. For more detail on data acquisition
see (self-identifying citation omitted).
-----------------------------------
Insert Table 1. about here
-----------------------------------
In addition to the aforesaid data collection efforts, two of the authors visited several crime
labs and observed forensic scientists in their places of work. These were not full-fledged
ethnographies. However, the observers were nonetheless able to get a far richer appreciation of
the localized settings, inter-lab variations in culture and practice, and work functions of scientists
in different labs (Emerson, Fretz, and Shaw 2001).
9
Qualitative data analysis. The analytic process was driven by the objective of making
sense of the data by identifying recurring patterns. To this end, we followed the coding and
analysis process described in Miles and Huberman (1994). All interviews were transcribed
verbatim, coded, and interpreted by the authors and a team of research assistants. Throughout the
data analysis process, we followed a procedure of iterating between our conceptual framework,
the data, and the literature (Strauss 1987).
All interviews were transcribed verbatim, and coded: First, each of our qualitative
research team members ―open coded‖ the interview transcripts as they became available and
labeled meaningful passages. Second, we then met as a team and discussed each label, resulting
in an initial list of codes (Miles and Huberman 1994). This list of codes was used by a team of
research assistants to subsequently code all interviews with the help of the qualitative research
software. Trained research assistants were instructed to code line by line; thus the smallest
coding unit was a single line of text. Each member of the research team also wrote memos or
notes about codes and their relationships, which became part of the data. One author periodically
met with the team of research assistants and discussed issues that arose during individual coding.
Once the initial coding was complete, a senior research assistant ran a comparison of coding to
ensure intercoder reliability; 85% of the codes were similarly identified by different coders. This
is considered an acceptable level of inter-coder reliability (Smith, Feld, and Franz 1992).
Discrepancies between the individual coders were discussed until an agreement was
reached. The third step entailed axial (or pattern) coding. This entails subsuming similar codes
into meta-codes (Miles and Huberman 1994). For example, we clustered all codes that referred to
the way individuals search for knowledge, what sources they turn to, and the rationales behind
those choices, into a meta-code entitled ―search strategies.‖ Finally, fourth, we constructed
10
matrix displays (Miles and Huberman 1994) to study the relationship between meta-codes and
select characteristics of the respondents.
Survey data. Surveys were administered to representatives of DNA labs in the United
States in December of 2005. Complete responses were received from 37. By implication we did
not receive responses from all states. Consequently, in Table A1 in the Appendix we tabulate and
test for differences between responding and missing states. Descriptive results suggest that, on
average, states that did respond tend to be larger and have more lab facilities and capacity than
those that did not. However, differences are not statistically significant.3
We then supplemented the survey data with external data from each state from
government sources. These external data include population and economic statics and dynamics,
geographic information, institutional (regional) affiliations, crime statistics, and information
about forensic lab size, capacity, and prior assistance to other labs. Using data from government
sources external to the survey mitigates concerns of common methods bias (Podsakoff,
MacKenzie, Jeong-Yeon, and Podsakoff 2003). Table 2 below provides detail concerning each
of the specific datum, its source, its mean and standard deviation.
Directed knowledge sharing ties across states. Survey participants were asked the
following question to determine which labs in which states outside their own they contacted for
assistance: ―When your laboratory seeks additional expertise in difficult DNA cases (e.g., trace
evidence, DNA mixtures, kinship analysis) in which labs are the individuals you would typically
consult?‖ Respondents were asked to check next to each of the 50 US States and the District of
Columbia if they contacted a lab therein for assistance. A dummy coded variable is thus
specified such that (Xij = 0 or Xij= 1, but Xij does not necessarily = Xji). That is, assistance seeking
3 States for which we have consequential ―missingness‖ include: Alaska, DC, Florida, Idaho, Kentucky, Maine,
Michigan, Mississippi, Nevada, North Carolina, South Dakota, Tennessee, Washington, and Wyoming.
11
is construed as directed—lab i may seek the assistance of lab j but lab j need not seek the
assistance of lab i.
Explanatory variables. Based on our initial qualitative and observational data collection
we developed an appreciation for the importance of professional association meetings for
establishing and maintaining contacts and accessing new knowledge. These meetings are foci
(Feld 1981). We thus expect to observe a positive relationship between the number of meetings
that corporate actors of labs in different states both attended and the likelihood that a knowledge
sharing tie exists between the states’ labs. To test this we compute a variable ranging from 0 to 6
(mean = 3.658; SD = 1.473) denoting the number of various professional conferences scientists
in labs i and j both attended in the past two years.
We should observe that laboratories in adjoining states such as New York and New
Jersey are more likely to have a directed knowledge sharing tie than New York and Alabama.
One reason for this is the movement of personnel between laboratories, as indicated in our
qualitative data analyses. Moreover, adjoining states are more likely to have collaboration
opportunities as a result of regional crime patterns (Stouffer 1940; Brantingham and
Brantingham 1981; Canter and Gregory 1994; Harries 1999). It thus follows that to the extent
that offenders commit crimes in other jurisdictions (states) than those in which they reside, they
are more likely to do so in: (a) adjacent states, and/or (b) jurisdictions that are closer.
Consequently, labs are more likely to have DNA hits for offenders in adjacent or close states,
and are thus more likely to have knowledge sharing ties with such labs. We thus expect that
laboratories in different states are more likely to have knowledge sharing ties with laboratories
in other states as a function of proximity (see, e.g., Sorenson and Stuart 2001; Kalnins and
Chung 2006). A dummy variable denoting states i and j are adjacent is thus calculated (mean =
12
0.09; SD = 0.286). We also create vectors of measures to capture location differences and those
pertaining to similarity across other characteristics. For geography we include (ln) distance
(mean = 6.772; SD = 0.809); and dummy variables indicating that states i and j are in the same
Census region (mean = 0.234; SD = 0.424) or Health and Human Services Region (mean =
0.089; SD = 0.234) respectively.
From a functional perspective, labs in states with a greater capacity to assist with
investigations should be more likely to be contacted by other states‘ labs for assistance. We thus
compute measures that get at each state‘s labs‘ capacity to assist with investigations. These
measures include global mean-centered measures of the number of: investigations state j’s labs
assisted in 2003( #(investigations aided)j,’03 – Σ
), forensic samples
(#(forensic samples)j,’03 – Σ
), and offender profiles (#(offender profiles)j,’03 –
Σ
) in j’s databases.
To capture preferential attachment or a Matthew Effect (Yule 1925; Merton 1968; 1985;
1988; Havemann, Heinz, Wagner-Döbler 2005) we include a measure of each alter states‘ labs
indegree that is centered around the global mean: I(deg)j – Σ
. This mechanism implies
that net of functional differences accounted for with the measures outlined above, we should
observe that labs in given states (i) are more likely to seek counsel from labs in different states
( j) if other states ( m) also seek counsel from j.
We also calculate measures that vary over ego and alter dyads concerning the relative
crime and economic conditions in each state to measure similarities (homophily) and differences
in the states‘ crime, social, and economic conditions that may compel one state‘s labs to seek
counsel from labs in another state. These measures include: differences in per capita income
13
between states i and j at time t-1 (|Per capita incomei,‘04 – Per capita incomej,‘04|); comparative
changes in population growth rates between states from t-2 to t-1 (|∆(% pop. growth)i,‘03-‗‘04 –
∆(% pop. growth)j, ‘03-‘04|); the absolute difference in the number of hate crimes (|# hate crimesi,‘04
– # hate crimesj,‘04|), comparative changes in murder rates (|∆(%murders)i,‘03‘-‘04 –
∆(%murders)j, ‘03-‘04|), and rapes (|∆(%rapes)i,‘03-‘04 – ∆(%rapes)j, ‘03-‘04|). We focus on these
crimes because of their relative prominence, as well as the importance of DNA evidence in
apprehending and convicting perpetrators in such cases. We also calculate similar metrics
specific to each alter state, j (# hate crimesj, ‘04, ∆(%murders)j, ‘03-‘04, ∆(% rapes)j, ‘03-‘04), based on
the intuition that states with higher rates of these crimes may receive more public exposure.
These metrics are all lagged based on the nature and availability of the data. The length of the
respective lag of each measure is noted in table 2 below, along with a description of the measure
along with its source. Finally, as a structural control, we specify a dummy variable indicating
reciprocity (Xij= Xji=1), which occurs quite rarely in our data (mean = 0.009; SD = 0.094).
---------------------------------
Insert Table 2. about here
---------------------------------
Statistical model. We use quantitative data to determine which factors lead a lab in a
specific state to contact a lab in a different state for counsel and advice on difficult cases.
Modeling this process is complicated because of structural autocorrelation arising from the
dependency between observations in the same row or column (Krachhardt 1988). We thus
employ an MRQAP model (a linear probability model in this context) that is robust to
multicollinearity and skewness using the double semi-partialling method proposed by Dekker,
Krachhardt, and Snijders (2007). P-values are computed using 2000 – 3000 permutations.
PRESENTATION OF FINDINGS
14
----------------------------------
Insert Figure 2. about here
----------------------------------
The social structure of inter-state DNA lab knowledge sharing
Figure 2 is a graphical depiction of the digraph. Some basic descriptive statistics of the
digraph indicate one large component and one isolate (South Carolina). The average geodesic
distance (among reachable dyads) is 3.287. The mean raw (normalized) indegree is 2.622
(7.282), with a standard deviations of 2.654 (7.373). These figures indicate that most states do
not have a significant amount of contact with labs in other states, but there are a few labs that
seek or provide counsel to several other states. Outdegree (indegree) network centralization is
29.63% (23.92%), which suggests that directed ties are not concentrated around a few hubs.
Finally, measures of centrality indicate that states such as California, Texas, Illinois, and
Pennsylvania are frequently cited as states housing labs to which labs in other states turn to for
counsel with difficult cases. The state of Virginia has the largest indegree. In order to determine
what factors lead to these ties, we turn now to the regressions.
----------------------------------
Insert Table 3. about here
----------------------------------
Table 3 above presents coefficients from MRQAP linear probability regressions. The first
model includes variables measuring propinquity and geography. As noted above, because crimes
tend to be perpetrated within close proximity to offenders‘ homes (Stouffer 1940; Brantingham
and Brantingham 1981; Canter and Gregory 1994; Harries 1999), it follows that to the extent that
offenders commit crimes in other jurisdictions (states) than those in which they reside, they are
more likely to do so in: (a) adjacent states, and/or (b) states that are geographically closer based
15
on distance. Consequently, labs should be more likely to have DNA hits for offenders in adjacent
or close states, and should thus more likely to have knowledge sharing ties with such labs. Our
quantitative evidence substantiates this intuition. Sharing a border with a state (adjacency)
increases the probability of also seeking counsel from a DNA lab in that states (b = 0.184; SE =
0.178; p <.001 (two-tailed test)). Similarly, if a state lab is in the same Census region as that of
another state it is also more likely to seek counsel from a lab in that other state (b = 0.031; SE =
0.121; p <.1). On the other hand, (ln) distance between states decreases this likelihood (b = -
0.019; SE = 0.081; p <.1). Figure 4 below depicts a potential reason for this pattern that is
consistent with the story outlined above. The figure illustrates the number of DNA ―hits‖ in
Massachusetts‘ that can be matched to those in other states. Most of the hits are concentrated in
the Northeast in states that are adjacent to Massachusetts.
----------------------------------
Insert Figure 4. about here
----------------------------------
The primary source of social connections and knowledge transfer—mentioned by nearly
every one of our informants in the qualitative interviews—is attendance at several different
professional meetings. Co-attendance at professional meetings is important for several reasons.
First, it affords the opportunity to deepen and broaden existing social relationships. Second, it
provides a venue for the exchange of best practices and emerging knowledge. Finally, meetings
are a focus (Feld 1981) or setting (Sorenson and Stuart 2008) where new ties can form—ties that
may extend beyond geographic areas.
Models 2 and 3 include the measure of the number of professional meetings
representatives of labs in states i and j both attended. Model 2 is an unconditional model and
model 3 includes the measures of propinquity and geography. The results of both models
16
indicate that as the number of meetings i attended that j also attended increases, so too does the
probability that i seeks help from j in difficult cases (model 2: b=0.016; SE = 0.037; model 3:
0.014; SE = 0.086; both p <. 05).4
Among the most consistent findings in the various literatures concerning networks is that
they tend to exhibit power-law distributions (Barabási and Albert 1999; Newman 2005; Clauset,
Shalizi, and Newman 2009). These distributional realizations may be the result of preferential
attachment processes or a Matthew Effect (Yule 1925; Merton 1968; 1985; 1988; Havemann,
Heinz, Wagner-Döbler 2005). In our context such processes imply that a lab in state i is more
likely to seek counsel in difficult cases from labs in state j (but not necessarily vice versa) if that
lab is more frequently contacted by other labs (m) for advice. This may be due to past
performance differences or those pertaining to perceptions thereof. An example of the essence of
this process playing out in our research site is recounted by a deputy director in a NY lab, who
was asked about who he contacts for information. His response is telling: ―Usually it‘s a
telephone call or an email to colleagues that I know. I find the information is more the opposite.
[i.e., people usually contact NYC rather than vice versa].‖ Model 3 includes a measure of
indegree for state j. The estimate suggests that states (i) are indeed more likely to seek counsel
from labs in another state (j) if other states‘ labs (m) also seek counsel from labs in state j (b =
0.025; SE = 0.078; p <.001).
Functional differences and perceptions thereof may not be tightly coupled (Merton 1968).
Our quantitative data allow for a nice distinction of the two. We have data on directed (indegree),
as just noted. Additionally, we have quite granular data on the economic, social, and
criminological characteristics of different states. We also collected data on each state‘s lab-
4 A squared term could not be estimated along with the main effect due to excessive collinearity.
17
specific capabilities (number of samples, offender profiles) and prior level of investigations
aided that enable us to directly measure and thus distinguish states‘ labs actual ability and past
willingness to assist with investigations. Not surprisingly, these coefficients are correlated with
an ―alter‖ (j) lab‘s indegree: .70 with the number of offender profiles; .67 with the number of
forensic samples; and .70 with the number of investigations aided. These correlations suggest
that directed ties seeking counsel on hard cases are indeed informed by the ability and
willingness of a target lab to offer assistance. There is, however, an important and sizeable effect
that cannot be attributed to these functional concerns that implicates other processes.
In model 5 we specify an unconditional model that includes three measures that capture
state j‘s functional capacity and past willingness to assist with DNA cases. The evidence
suggests that labs are more likely to contact labs in other states for assistance if they have
evidence that state has a greater than average willingness to aid in investigations. The same is
true if it has a larger than average number of offender profiles. However, once the other
measures are included (in model 6) these effects no longer achieve statistical significance at
conventional levels. Estimates in model 6 also suggest that the comparative crime, economic,
and social conditions of the states does not have a bearing on the likelihood of seeking assistance
across states. Finally, there is strong evidence that labs are likely to seek counsel on difficult
cases from labs that seek counsel from them (b = 0.786; SE = 0.061; p < .001). That is, there is
strong tendency towards reciprocity.
The quantitative evidence provides a picture of the social structure of state-run DNA
laboratories in the United States in and around 2005. It also describes what factors are associated
with assistance seeking across states. Results indicate that propinquity and geography matter.
Having a place to meet and socialize also matters, as does preferential attachment. These data do
18
not, however, afford leverage to open the black-box of tie formation. We now turn to the
qualitative evidence that does.
Search for knowledge
Analytical technologies constantly evolve. As a result, scientists in the labs we studied
are compelled to constantly update their knowledge. In coding the qualitative responses we
observed a consistent pattern concerning the sequence of knowledge search: technicians first
reviewed documents (internal manuals, audit documents, and professional websites). If this
search failed to yield answers, they would then seek the counsel of experts within their labs on
the matter. For example, Bob*5, an assistant in a NY lab, put it:
[…] we have a chain of command, if I had a question, or a concern about a policy that I
feel like is not in existence for example, like the manual doesn’t address something, and
I’ve never been trained in how to address something, so I would go to [supervisor’s name
excluded] first. Although, I kind of know in the back of my head, she’s not going to know
the answer to this, or she doesn’t have the discretion to decide, okay, you’re right, there’s
no policy, this is the policy. She’s going to have to take my question to[leader’s name
removed] who’s the technical leader […]
This was more frequently the case in the largest labs that had the most internal resources. It also
serves as a meaningful baseline when interpreting other findings. That is, a knowledge search
process that begins by referring to codified material.
Figure 1 provides a stylized depiction of how individual organizational actors
(employees) develop social capital that: (1) spans organizational boundaries; and (2) links
laboratories, thus allowing knowledge and assistance flow. This figure is based on the qualitative
evidence from our case as well as prior research.
5 This and all other names are pseudonyms.
19
Social capital formation
The primary source of social connections and ultimately knowledge transfer—mentioned
by nearly every one of our informants—is attendance at professional meetings. This co-
attendance can lead to knowledge transfer and the establishment of individual, scientist-level ties
between labs that that are otherwise unlikely given the geographic distance and size differences
of labs; for example, an inter-organizational link between a large lab in NY and one in a small
state established by individual scientists. As Jonah of a NY lab states:
[…] No, I mean the social aspects can’t be ignored. That’s one of the biggest perks of
going to those things. But going to the conferences and listening to the lectures and
seeing the post presentations, and seeing what people are doing out there is amazing,
‘cause there are some people in these Podunk little college towns that are doing forensic
research that is cutting edge stuff and it’s great. We see these posters and we’re blown
away. And we come back here and we say listen, we have to call that guy and talk to him
about his, whatever he developed, protocol, procedures, whatever, and see if we can
implement that in this laboratory ‘cause it’s feasible, we can do that.
Professional association meetings are thus an important focus (Feld 1981) in which relationships
form and strengthen. Consequently, lab administrators use them strategically—an investment in
sociability (Bourdieu 1985; Lin 1991) that leads to social capital. Claire, a CODIS administrator
stated it thusly:
We had 2 people at that meeting. I try and send as many people as I can to conferences
like that. Not just to listen to the papers, but also it’s a good way to meet up with
everyone else in the other labs and share stories and get information. So yeah, we attend
as many conferences as we can.
John, a deputy director, also stresses the social aspect of meetings
But if people have met at meetings—what I find in the meeting, it’s not always the content
that’s important, but also the contacts that I make, they’re both important. I meet my
colleagues at these various meetings, and then we network at the meetings. So I find I get
as much out of dinner, lunch and talking to people as I do for the scientific sessions. And
20
that’s not to say the meeting’s not important for scientific sessions, because I don’t want
to be quoted that way.
It is important to note that many of the meetings are funded by either the government or
commercial vendors who sell the analytical technologies used by the scientists. This funding
makes it possible for many of the scientists to attend the conferences who might not otherwise be
able to. This institutional structure is thus a vital force that helps foster the conditions for social
tie formation and subsequent knowledge diffusion across labs.
Micro social mobility and macro knowledge flow: The important of audits and employee mobility
Prior research has revealed how champions such as consultants are often the conduits of
the diffusion of new practices (e.g., Covaleski and Dirsmith 1988; DiMaggio 1991; Sitkin,
Sutcliffe, and Schroeder 1994).Our fieldwork suggests a similar mechanism: institutional audits.
Institutional audits are a particularly interesting basis of knowledge transference across
organizations. Audits are typically conducted by seasoned individuals in the field. When they
visit a site they thus serve multiple purposes. First, they serve a role of auditor by evaluating
laboratories. In the process they also afford access to their stocks of knowledge gleaned from
their own site and the other sites they have visited. Second, a visit to a site affords the
opportunity for an interpersonal connection (much like academic talks and subsequent one-on-
one faculty meetings). These connections can serve as the link connecting two laboratories
across states that serves as a conduit for knowledge transfer across space. Cathy describes this
process thusly:
I think we have one of the most thorough training programs in the country, and I base
that on audits of other laboratories, and I’ve done about 50 audits of different
laboratories. There’s another source of contacts that I have and a good base knowledge
of who does what. That helps me out a lot there, those audits. I always go to the
experienced people first. And I have experiences in some areas and I’m able to answer
those questions, but areas I don’t, I try to find people and identify people when I go out to
21
meetings that have experiences in areas that I don’t ‘cause I can’t be experienced in
every area; there’s just not enough time in the day.
Prior research shows that the localization or movement of individuals (micro social units
of analysis) across salient macro-social divides has a significant bearing on the concentration,
distribution, and diffusion of knowledge. This transfer can occur when employees or owners
from one firm make lateral moves to other firms, or found their own firms (Argote and Ingram
2000; Phillips 2002; Song, Almeida and Wu 2003). For example, the interorganizational
mobility of scientists can lead to the localization of knowledge within macro-level social
boundaries such as regions (Saxenian 1994; Almeida and Kogut 1999; Rosenkopf and Almeida
2003). Structural rules that inhibit this movement can thus have implications for knowledge
flows across organizations (Marx Strumsky, and Fleming 2009). Similarly, the mobility of
individuals can also lead to the dissolution of the linkages among macro social entities. The
movement of professionals from one firm, A, to another, B, for example, often leads to the
dissolution of an existing business relationship between firms (e.g., between A and C) as
manager, i, moves from firm B to firm C (see, e.g., Broschak 2004; see also Baker, Faulker, and
Fisher 1998). We also find that inter-organizational and inter-state knowledge transfer originates
from the movement of colleagues across labs. Brandon, a DNA tech leader, put it thusly:
Well it comes in one of two groups, and that’s folks that I came to know personally, by
working with them. Two of my former bosses are on that list. Actually I guess three of my
former bosses are on that list. […]
SUMMARY & CONCLUSIONS
Significant attention has been devoted to the patterns and consequences of boundary
spanning activities, information search and sharing, and isomorphism of organizational
knowledge and practice. In much of this research organizations are viewed as social actors in and
of themselves that engage in this boundary spanning activity. This focus is sensible when
22
analyzing the patterns of organizational ties. However, it is not especially well-suited to the study
of the mechanisms governing how and why organizations develop social capital and span
boundaries in the first place. In this research we unpacked the black box of organizational social
capital formation boundary spanning activity. By using unique qualitative and quantitative data
from a knowledge intensive industry—US government crime laboratories involved in DNA
analysis—we identified several mechanisms leading to boundary spanning activity. The
quantitative data we provide a picture of several of the mechanisms, as well as the antecedents of
isomorphism processes. Results reveal that structural features, chance meetings, and the initiative
of lab leaders (and to a lesser extent subordinates) lead to boundary spanning activity, access to
distinct pools of knowledge, and, ultimately, isomorphism. Thus social structure exhibits
significant subtleties. On one hand, structural forces clearly contour and condition the nature of
knowledge flow across organizational boundaries. Institutional forces create the conditions for
tie formation by providing funding for scientist travel to professional conferences, or by dictating
the timing and actors involved in institutional audits. However, this funding does not, in itself,
lead to tie formation. That requires the action of individuals in pursuit of knowledge.
23
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FIGURE 1. STYLIZED DEPICTION OF INTERPLAY BETWEEN LEVELS OF ANALYSIS
Macro-level (labs/states)
Micro level (―corporate agents‖)
Note:
1. Mechanisms (e.g., contracts, trade) linking macro social entities
2. Mechanisms linking macro social entities to micro entities (e.g., employment contracts)
3. Relationships between micro social entities
4. Link between macro social entities enabled by micro level (corporate agents)
1
2 2
3
4
29
FIGURE 2. DIRECTED KNOWLEDGE SHARING TIES BETWEEN DNA LABS IN THE USA, 2005
Note: Free-form Kamada-Kawai algorithm used for graph layout.
Average geodesic distance (among reachable dyads) = 3.287
Overall (weighted) graph clustering coefficient=0.147 (0.117)
Mean (normalized) indegree = 2.622 (7.282); standard deviations = 2.654 (7.373)
Network centralization (outdegree) = 29.63%
Network centralization (indegree) = 23.92%
30
FIGURE 3. STATES WEIGHTED BY NUMBER OF DNA MATCHES WITH SAMPLES FROM
MASSACHUSETTS
31
FIGURE 4. STYLIZED DEPICTION OF MECHANISMS LEADING TO KNOWLEDGE DIFFUSION
Individuals Mechanism Organizational ties
(a)
(b)
(c)
(d)
(e)
(f)
Note: Mechanisms ―A,‖ ―B,‖ and ―C‖ identified in our field work; mechanisms ―D,‖ ―E,‖ and ―F‖ identified in prior
literature and our quantitative analyses. Lowercase a and b represent individual corporate actors (e.g., CODIS
administrators); i and j denote labs.
Meetings/
Conferences
b
a
b
a
Organization
audit
b
a
j
i
Employment
transition
b
a
j
i
j
i
b
a Geography
b
a Homophily
j
j
Preferential attachment
j
j
b
a
32
TABLE 1. OVERVIEW OF INTERVIEWS CONDUCTED
Professional role* Affiliation: State lab Affiliation: Local lab Total
Lab director 8 2 10
Technical leader 7 3 10
CODIS administrator 8 5 13
Total 23 10 33
* In cases where individuals hold multiple roles, the highest-ranking role is indicated.
33
TABLE 2. DESCRIPTION OF KEY VARIABLES
Variable Mean S.D. Year Source
Knowledge sharing tie between states
(ij,ji, Xij≠Xji,)…………………………………….......…………
0.073
0.260
2005
Survey
Σ professional meetings employees of states i and j co-
attended……………… ………………………………………………
3.658
1.473
2003 – 2005
Survey
Reciprocity (Xij=Xji=1)……………………….……………….
I(deg)j – Σ
……………………………………………..
0.009
0.000
0.094
2.761
2003 – 2005
2003 – 2005
Survey
Survey
State j’s capacity to assist with investigations:
#(investigations aided)j,’03 – Σ
#(forensic samples)j,’03 – Σ
……....
#(offender profiles)j,’03 – Σ
……....
Comparative and state-specific dynamic |(i – j)| crime
environments:
| # hate crimesi,‘04 – # hate crimesj, ‘04|…..……………….
|∆(% murders)i,‘03‘-‘04 – ∆(% murders)j, ‘03-‘04|……......…..
0.000
0.000
0.000
210.33
17.121%
1210.919
4174.435
117495.945
299.667
26.294%
2003 – 2004
FBI Uniform Crime Reports/
DOJ/FBI/CODIS
|∆(% rapes)i,‘03-‘04 – ∆(% rapes)j, ‘03-‘04|………....….....….
# hate crimesj, ‘04……………...………………….…….... ∆(% murders)j, ‘03-‘04………….......……...……….………
∆(% rapes)j, ‘03-‘04………………………..……………….
10.108%
165.243
-0.608
1.773
10.492%
255.359
16.485
10.162
Comparative state economic and population metrics:
|Per capita incomei,‘04 – Per capita incomej,‘04|…....….….
|∆(% pop. growth)i,‘03-‗‘04 – ∆(% pop. growth)j, ‘03-‘04|..….
$5,495.79
0.631%
$4,149.43
0.55%
2003 – 2004
Regional Economic Information
System, Bureau of Economic
Analysis, US Department of
Commerce
Geography and institutional measures:
State-to-state distances (LN distance)………..…….……
6.772
0.809
2005
GIS
States i and j share a border (i.e., are adjacent)………... 0.090 0.286 2005 U.S. Census
States are in the same Census region…………………….. 0.234 0.424 2005 U.S. Census
States are in the same Health and Human
Services region……………...………………,…………….…
0.089
0.284
2005 U.S. Dept of Health and Human
Services
Note: Italicized variables are dichotomous (0/1). Statistics calculated at the state or dyadic level (indicated by ―survey‖).
34
TABLE 3. MRQAP REGRESSION COEFFICIENTS PREDICTING DIRECTED TIES BETWEEN DNA LABS IN STATES I AND J
MODEL:
VARIABLE:
1.
Propinquity
b(SE)
2.
Co-
attendance
at meetings
b(SE)
3.
Propinquity +
Co-attendance
b(SE)
4.
Indegree
b(SE)
5.
Functional
Considerations
b(SE)
6.
States‘ crime,
Economic, and
Population trends
b(SE)
7.
Final model
b(SE)
States i and j are
adjacent………........................
0.184***
(0.178)
0.181***
(0.14)
0.176***
(0.124)
0.135***
(0.212)
States are in same Census
region……………………….…...
0.031#
(0.121)
0.033*
(0.095)
0.036*
(0.083)
0.02
(0.204)
LN(Distance) between
states i and j…...........………..
-0.019#
(0.081)
-0.019#
(0.083)
-0.016
(0.068)
-0.016#
(0.203)
Σ of meetings i and j co-
attended……………………....
Reciprocity (Xij=Xji=1)………
I(deg)j – Σ
…………....
#(investigations aided)j,’03
– Σ
…
#(forensic samples)j ,’03
– Σ
……
#(offender profiles)j ,’03
– Σ
….....
|# hate crimesi,‘04 –
# hate crimesj, ‘04|…..................
0.016**
(0.037)
0.014*
(0.086)
0.011*
(0.083)
0.025***
(0.078)
0.000*
(0.031)
-0.000
(0.031)
0.000**
(0.031)
0.000
(0.056)
0.009*
(0.184)
0.786***
(0.061)
0.024***
(0.197)
0.000
(0.203)
-0.000#
(0.202)
0.000#
(0.200)
-0.000
(0.187)
35
|∆(%murders)i,‘03‘-‘04 –
∆(%murders)j, ‘03-‘04|.................
|∆(%rapes)i,‘03-‘04 –
∆(%rapes)j, ‘03-‘04|…………...
|Per capita incomei,‘04 –
Per capita incomej,‘04|………
|∆(% pop. growth)i,‘03-‗‘04 –
∆(% pop. growth)j, ‘03-‘04|..........
Intercept……………………...
0.180
0.014
0.123
0.116
0.072
-0.000
(0.05)
-0.002**
(0.04)
-0.000**
(0.039)
-0.022#
(0.043)
0.013
0.000
(0.21)
-0.000
(0.206)
-0.000
(0.191)
-0.014
(0.197)
0.138
R2
Adjusted R2
Probability
# of permutations
# of observations
0.072
0.070
0.000
2000
1332
0.008
0.008
0.012
2000
1332
0.078
0.076
0.000
2000
1332
0.151
0.148
0.000
2000
1332
0.038
0.036
0.000
2000
1332
0.017
0.014
0.000
2000
1332
0.234
0.227
0.000
3000
1332
Source: Unique data; see table 2 above for a description of the data sources.
Note: Unstandardized coefficients presented. MRQAP computed via Double-Dekker Semi-Partialling. Italicized variables are dichotomous (0/1).
#P < .05 (one-tailed test)
*P < .05 (two-tailed test)
**P < .01 (two-tailed test)
***P<.001 (two-tailed test)
36
APPENDIX
TABLE A1. EVALUATION OF POTENTIAL SAMPLE SELECTION
Sources: See table above for sources of each measure.
Note: T-tests performed assuming equal or unequal variance (more liberal results shown, i.e.,
those more likely to evidence a difference). States (+DC) for which we have consequential
―missingness‖ include: Alaska, DC, Florida, Idaho, Kentucky, Maine, Michigan, Mississippi,
Nevada, North Carolina, South Dakota, Tennessee, Washington, and Wyoming.
#P <.1 (two-tailed test)
*P <.05
**P <.01
***P <.001
VARIABLE RESPONSE MISSING RESPONSE
Mean
SE
Mean
SE
t-test
p value
Population, 2004…………… 6,241,057 1,154,646 4,481,164 1,297,474 0.312
∆ population, '03-'04……….. 0.810 0.097 1.162 0298 0.279
∆ % murder, '03 - '04….…… -0.608 2.748 4.943 6.982 0.469
# of hate crimes, '04……….. 169.833 43.504 109.643 39.885 0.314
# of labs in state……………. 3.865 0.764 2.357 0.589 0.125
# of offender profiles, '03….. 84,070.46 19,582.66 61,727.14 25,408.78 0.492
# of forensic samples, '03…. 3513.27 695.739 2128.143 900.487 0.233
# of investigations aided, '03.
N
922.514
37
201.82 642.357
14
370.03 0.513
37
TABLE A2. ROBUSTNESS CHECKS: MRQAP REGRESSION COEFFICIENTS PREDICTING
DIRECTED TIES BETWEEN STATE I AND J’S DNA LABS
Model Description Key
estimates,
b(SE)
Model Notes
A1 Model 1 w/ HHS regions instead of
Census regions
0.018(0.118), ns MRQAP
A2 Model with attributes of i:
∆(# rapes)i,‘03-‗04
# hate crimesi,‘04
Per capita incomei, ‗04
-0.000(0.025),ns
-0.000(0.031), ns
0.000(0.033), ns
MRQAP
A3
A4
Model with attributes of j:
∆(# rapes)j,‘03-‗04
# hate crimesj,‘04
Per capita incomej, ‗04
Model 7 with # hate crimesj,‘04
0.000(0.012), ns
0.000(0.019)*
0.000(0.023), ns
-0.000(0.054),ns
MRQAP
MRQAP
All other coefficients
consistent with
underlying model
